- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | WANG Huiqin, GUO Ruili, HE Yongqiang, LIU Bincan. NGO-Based CNN-BiLSTM-AM Model for Landslide Displacement Prediction[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20240550
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A convolutional-bidirectional long short-term memory neural network-attention mechanism (CNN-BiLSTM-AM) prediction model optimized by the northern goshawk optimization (NGO) algorithm for landslide displacement was proposed to address challenges that a single prediction model fails to effectively extract complex sequence features and that manual parameter tuning tends to fall into local optima in current landslide displacement prediction research. Firstly, according to the factors affecting the landslide, the multivariate empirical mode decomposition (MEMD) algorithm was used to decompose various landslide displacement data into trend and periodic components. The trend components were predicted using the autoregressive integrated moving average (ARIMA) method. For the periodic components, influencing factors were identified through the gray correlation degree, and a CNN-BiLSTM-AM combined model was constructed for prediction. The optimal hyperparameters of this model were obtained through NGO. Then, by considering the lag of the periodic components, the Spearman correlation coefficient was used to select the optimal lagged displacement to further enhance the model’s predictive performance. Finally, the model was validated using monitoring data of the Tuojiashan Landslide in Weiyuan, Gansu Province. The results show that the RMSE and MAE of the total displacement prediction of the Tuojiashan landslide are as low as 0.22 mm and 0.37 mm, respectively, showing the prediction accuracy of the correction, while the R2 reaches 0.98, which fully verifies the validity and reliability of the proposed model in landslide displacement prediction.
| [1] |
邢保印, 张炜怡, 章广成, 等. 基于变形速率分解的阶跃型滑坡预测——以呷爬滑坡为例[J]. 岩石力学与工程学报, 2023, 42(3): 685-697.
XING Baoyin, ZHANG Weiyi, ZHANG Guangcheng, et al. Prediction of step-type landslides based on deformation rate decomposition: a case study of Gapa landslide[J]. Chinese Journal of Rock Mechanics and Engineering, 2023, 42(3): 685-697.
|
| [2] |
高雅萍, 陈曦, 涂锐. 顾及降雨影响的动态优化时滞时序GM(1, 2)模型在滑坡位移预测中的应用[J]. 测绘学报, 2022, 51(10): 2183-2195. doi: 10.11947/j.AGCS.2022.20220290
GAO Yaping, CHEN Xi, TU Rui. Application of dynamic optimization time-delay GM(1,2) model in landslide displacement prediction considering the influence of rainfall[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 2183-2195. doi: 10.11947/j.AGCS.2022.20220290
|
| [3] |
李骅锦, 许强, 何雨森, 等. “阶跃式” 滑坡位移预测及阈值分析的ARMA-(LASSO-ELM)-Copula模型[J]. 岩石力学与工程学报, 2017, 36(增2): 4075-4084.
LI Huajin, XU Qiang, HE Yusen, et al. ARMA-(LASSO-ELM)-Copula model for “step” landslide displacement prediction and threshold analysis[J]. Chinese Journal of Rock Mechanics and Engineering, 2017, 36(S2): 4075-4084.
|
| [4] |
DENG L Z, SMITH A, DIXON N, et al. Machine learning prediction of landslide deformation behaviour using acoustic emission and rainfall measurements[J]. Engineering Geology, 2021, 293: 106315 doi: 10.1016/j.enggeo.2021.106315
|
| [5] |
NAVA L, CARRARO E, REYES-CARMONA C, et al. Landslide displacement forecasting using deep learning and monitoring data across selected sites[J]. Landslides, 2023, 20(10): 2111-2129. doi: 10.1007/s10346-023-02104-9
|
| [6] |
李达, 瞿伟, 张勤, 等. 融合多层感知机和优化支持向量回归的滑坡位移预测模型[J]. 武汉大学学报(信息科学版), 2023, 48(8): 1380-1388.
LI Da, QU Wei, ZHANG Qin, et al. Landslide displacement prediction model integrating multi-layer perceptron and optimized support vector regression[J]. Geomatics and Information Science of Wuhan University, 2023, 48(8): 1380-1388.
|
| [7] |
杨背背, 殷坤龙, 杜娟. 基于时间序列与长短时记忆网络的滑坡位移动态预测模型[J]. 岩石力学与工程学报, 2018, 37(10): 2334-2343.
YANG Beibei, YIN Kunlong, DU Juan. A model for predicting landslide displacement based on time series and long and short term memory neural network[J]. Chinese Journal of Rock Mechanics and Engineering, 2018, 37(10): 2334-2343.
|
| [8] |
ZHANG Y G, TANG J, CHENG Y, et al. Prediction of landslide displacement with dynamic features using intelligent approaches[J]. International Journal of Mining Science and Technology, 2022, 32(3): 539-549. doi: 10.1016/j.ijmst.2022.02.004
|
| [9] |
SONG K L, YANG H Q, LIANG D, et al. Step-like displacement prediction and failure mechanism analysis of slow-moving reservoir landslide[J]. Journal of Hydrology, 2024, 628: 130588. doi: 10.1016/j.jhydrol.2023.130588
|
| [10] |
于群, 霍筱东, 何剑, 等. 基于斯皮尔曼相关系数和系统惯量的中国电网停电事故趋势预测[J]. 中国电机工程学报, 2023, 43(14): 5372-5381.
YU Qun, HUO Xiaodong, HE Jian, et al. Trend prediction of power blackout accidents in Chinese power grid based on Spearman’s correlation coefficient and system inertia[J]. Chinese Journal of Electrical Engineering, 2023, 43(14): 5372-5381.
|
| [11] |
ZHANG J R, LIN C Y, TANG H M, et al. Input-parameter optimization using a SVR based ensemble model to predict landslide displacements in a reservoir area–A comparative study[J]. Applied Soft Computing, 2024, 150: 111107. doi: 10.1016/j.asoc.2023.111107
|
| [12] |
袁维, 孙瑞峰, 钟辉亚, 等. 阶跃型滑坡综合变形预测及监测预警方法研究[J]. 水利学报, 2023, 54(4): 461-473.
YUAN Wei, SUN Ruifeng, ZHONG Huiya, et al. Research on comprehensive deformation prediction and monitoring and early warning method for step-like landslide[J]. Journal of Hydraulic Engineering, 2023, 54(4): 461-473.
|
| [13] |
王震豪, 聂闻, 许汉华, 等. 基于EEMD-Prophet-LSTM的滑坡位移预测[J]. 中国科学院大学学报, 2023, 40(4): 514-522.
WANG Zhenhao, NIE Wen, XU Hanhua, et al. Prediction of landslide displacement based on EEMD-prophet-LSTM[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(4): 514-522.
|
| [14] |
XU S L, NIU R Q. Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China[J]. Computers & Geosciences, 2018, 111: 87-96.
|
| [15] |
MURAWWAT S, ASIF H M, IJAZ S, et al. Denoising and classification of arrhythmia using MEMD and ANN[J]. Alexandria Engineering Journal, 2022, 61(4): 2807-2823. doi: 10.1016/j.aej.2021.08.014
|
| [16] |
MASSAR H, STERGIADIS C, NSIRI B, et al. EMD-BSS: a hybrid methodology combining empirical mode decomposition and blind source separation to eliminate the ocular artifacts from EEG recordings[J]. Biomedical Signal Processing and Control, 2024, 95: 106475. doi: 10.1016/j.bspc.2024.106475
|
| [17] |
TARMANINI C, SARMA N, GEZEGIN C, et al. Short term load forecasting based on ARIMA and ANN approaches[J]. Energy Reports, 2023, 9: 550-557.
|
| [18] |
LIN H J, ZHANG S R, LI Q H, et al. A new method for heart rate prediction based on LSTM-BiLSTM-Att[J]. Measurement, 2023, 207: 112384. doi: 10.1016/j.measurement.2022.112384
|
| [19] |
张振坤, 张冬梅, 李江, 等. 基于多头自注意力机制的LSTM-MH-SA滑坡位移预测模型研究[J]. 岩土力学, 2022, 43(增2): 477-486, 507.
ZHANG Zhenkun, ZHANG Dongmei, LI Jiang, et al. Study on LSTM-MH-SA landslide displacement prediction model based on multi-head self-attention mechanism[J]. Rock and Soil Mechanics, 2022, 43(S2): 477-486, 507.
|
| [20] |
ZHANG D D, CHEN B A, ZHU H Y, et al. Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model[J]. Energy, 2023, 285: 128762. doi: 10.1016/j.energy.2023.128762
|
| [21] |
NIU D X, YU M, SUN L J, et al. Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism[J]. Applied Energy, 2022, 313: 118801. doi: 10.1016/j.apenergy.2022.118801
|
| [22] |
包艳艳, 杨广泽, 陈伟, 等. 基于SBSS与CNN的750 kV变压器和尖板的放电信号声纹识别[J]. 西南交通大学学报, 2025, 60(3): 781-792.
BAO Yanyan, YANG Guangze, CHEN Wei, et al. Voiceprint recognition of discharge aliasing signals from 750 kV transformer and pin-plate based on sparse representation theory and convolutional neural network[J]. Journal of Southwest Jiaotong University, 2025, 60(3): 781-792.
|
| [23] |
王瑞, 张璐婷, 逯静. 基于新型相似日选取和VMD-NGO-BiGRU的短期光伏功率预测[J]. 湖南大学学报(自然科学版), 2024, 51(2): 68-80.
WANG Rui, ZHANG Luting, LU Jing. Short term photovoltaic power prediction based on new similar day selection and VMD-NGO-BiGRU[J]. Journal of Hunan University (Natural Sciences), 2024, 51(2): 68-80.
|
| [24] |
张凯, 张科, 保瑞, 等. 基于优化经验模态分解和聚类分析的滑坡位移智能预测研究[J]. 岩土力学, 2021, 42(1): 211-223.
ZHANG Kai, ZHANG Ke, BAO Rui, et al. Intelligent prediction of landslide displacements based on optimized empirical mode decomposition and K-Mean clustering[J]. Rock and Soil Mechanics, 2021, 42(1): 211-223.
|
| [25] |
张勤, 白正伟, 黄观文, 等. GNSS滑坡监测预警技术进展[J]. 测绘学报, 2022, 51(10): 1985-2000. doi: 10.11947/j.AGCS.2022.20220299
ZHANG Qin, BAI Zhengwei, HUANG Guanwen, et al. Review of GNSS landslide monitoring and early warning[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 1985-2000. doi: 10.11947/j.AGCS.2022.20220299
|
| [26] |
廖康, 吴益平, 李麟玮, 等. 基于时间序列与GWO-ELM模型的滑坡位移预测[J]. 中南大学学报(自然科学版), 2019, 50(3): 619-626.
LIAO Kang, WU Yiping, LI Linwei, et al. Displacement prediction model of landslide based on time series and GWO-ELM[J]. Journal of Central South University (Science and Technology), 2019, 50(3): 619-626.
|
| [27] |
张凯翔, 牛瑞卿, 胡友健, 等. 基于小波变换及外因响应的滑坡位移预测[J]. 中国矿业大学学报, 2017, 46(4): 924-931.
ZHANG Kaixiang, NIU Ruiqing, HU Youjian, et al. Landslide displacement prediction based on wavelet transform and external cause[J]. Journal of China University of Mining & Technology, 2017, 46(4): 924-931.
|
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